Intelligent detection method of working personnel wearing safety helmets in underground mine

Abstract

Visual image-based methods are currently a hot topic in intelligent detection of mine personnel wearing safety helmets. However, existing methods use limited underground mining data and the classification of safety helmet features is not accurate enough. By collecting images of actual production scenes such as underground mining sites and roadways, a mining helmet wearing dataset (MHWD) is constructed. The helmet wearing situation is further divided into three categories: correct wearing, non-standard wearing, and non wearing. YOLOX algorithm is used to detect personnel wearing helmets. In order to enhance YOLOX's capability to extract global features, the attention mechanism is introduced. The effective channel attention module is embedded in the spatial pyramid pooling bottleneck layer of YOLOX's backbone network. The convolutional block attention module is added after each upsampling and downsampling of the path aggregation feature pyramid network, thus the YOLOX-A model is built. By using MHWD, the YOLOX-A model is trained and validated. The results show that the YOLOX-A model can accurately identify the wearing of safety helmets by personnel in mine images with low illumination, blurriness, and personnel obstruction. The F1 scores for the classification results of non-standard wearing, correct wearing, and non wearing safety helmets are 0.86, 0.92, and 0.89, respectively. The average precision is 93.16%, 95.76%, and 91.69%. The average precision mean is 93.54%. The overall F1 score is 4% higher than the YOLOX model. The detection precision is higher than the mainstream target detection models EfficientDet, YOLOv3, YOLOv4, YOLOv5 and YOLOX.

Keywords

• underground mines
• target detection
• detection of personnel wearing safety helmets
• yolox
• attention mechanism